class_docstr: Cost function of a clustering problem.
  
  Parameters
  ----------
  points: np.ndarray
      k x n array where k is the number of points and n their coordinates
  num_clusters: int
      number of clusters to find
docstr: Clustering problem defined in
  Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
  https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  
  Parameters
  ----------
  name: str
      name of the dataset ("Ruspini", or "German towns")
  num_clusters: int
      number of clusters to find
  
  Note
  ----
  The MLDA problems are P1.a Clustering("Ruspini", 5) and  P1.b Clustering("German towns", 10)
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code: def from_mlda(
      cls, name: str, num_clusters: int, rescale: bool = True
  ) -> "Clustering":
      """Clustering problem defined in
      Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
      https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  
      Parameters
      ----------
      name: str
          name of the dataset ("Ruspini", or "German towns")
      num_clusters: int
          number of clusters to find
  
      Note
      ----
      The MLDA problems are P1.a Clustering("Ruspini", 5) and  P1.b Clustering("German towns", 10)
      """
      assert name in ["Ruspini", "German towns"]
      points = datasets.get_data(name)
      pb = cls(
          points=points, num_clusters=num_clusters, rescale=rescale
      )
      pb.add_descriptors(name=name)
      return pb
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 2 for calling the Clustering obj's underlying member method code with example usage: obj.from_mlda(cls, name, num_clusters, rescale) and returns: pb